Optimizing Convolutional Neural Networks Hyperparameters using Genetic Algorithm
In recent years, people from all over the world are suffering from one of the most severe diseases in the history, known as Coronavirus disease 2019, COVID-19 for short. When the virus reaches the lungs, it has a higher probability to cause lung pneumonia and sepsis. X-ray is a powerful tool in identifying the typical features of the infection for COVID-19 patients. The radiologists and pathologists observe that ground-glass opacity appears in the chest X-ray for infected patient, and it could be used as one of the criteria during the diagnosis process. In the past few years, deep learning has proven to be one of the most powerful methods in the field of image classification. Due to significantly differences of Chest X-Ray between normal and infected people, deep models could be used to identify the presence of the disease given a patient’s Chest X-Ray. Many deep models are complex, and it involves with lots of input parameters. Researchers sometimes struggle with the tuning process for deep models, especially when they build up the model from scratch. Genetic Algorithm, inspired from biological evolution process, plays a key role in solving such complex problems. In this paper, we proposed a genetic-based approach to optimize the Convolutional Neural Network(CNN) for the Chest X-Ray classification task. The code is available at this url
If you want to use the code, or find our project useful, you can set as follows:
@article{zhou2021heuristic,
title={Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm},
author={Zhou, Meng},
journal={arXiv preprint arXiv:2112.07087},
year={2021}
}s